Core ML Model Zoo
Collection
PyTorch models converted to Core ML for on-device inference on iPhone, iPad and Mac. • 46 items • Updated • 1
Face Parsing — Core ML
Facial Segmentation, 2019
Semantic face parsing into 19 regions: skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, cloth, background. 512×512 input.
Core ML conversion of zllrunning/face-parsing.PyTorch for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | image segmentation |
| Upstream | zllrunning/face-parsing.PyTorch |
| Packages | 1 |
| Download size | 47 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~300 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
FaceParsing.mlpackage.zip |
47 MB | all |
a6dd498bb4e19df1… |
| Total | 47 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
Download
hf download mlboydaisuke/coreml-zoo --include "faceparsing/*" --local-dir ./face_parsing
unzip './face_parsing/faceparsing/*.zip' -d ./face_parsing
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all // as converted — see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try FaceParsing(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
Demo
- Sample app — CoreML-Face-Parsing, a standalone iOS project.
- Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling):
docs/coreml_conversion_notes.md - Model index: CoreML-Models
License
The conversion inherits the upstream license: MIT.
Credits
- Upstream authors: zllrunning/face-parsing.PyTorch, 2019
- Core ML conversion: john-rocky (Daisuke Majima)
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